Marina Miller

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Journals/Publications

• Miller, M. D., & Devor, E. J. (2020). Integration of Clinical and Molecular Features into Prediction Models for Outcomes in Endometrial Cancer. Clinical obstetrics and gynecology, 63(1), 40-47.
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  Endometrial cancer recurrence carries a poor prognosis. The rising incidence of endometrial cancer calls for improvements in treatment of advanced and recurrent diseases. Efforts have been made to molecularly characterize endometrial cancer with the goal of improving therapies. The study presented here describes the utilization of molecular features of endometrial cancer tumors that are likely to recur, along with clinical characteristics utilized together to predict recurrence. This work further studies recurrent endometrial cancers to group them into "clusters" based on the tumor's molecular makeups with the ultimate aim to focus therapy on the molecular pathways potentially leading to recurrence.
• Miller, M. D., Devor, E. J., Salinas, E. A., Newtson, A. M., Goodheart, M. J., Leslie, K. K., & Gonzalez-Bosquet, J. (2019). Population Substructure Has Implications in Validating Next-Generation Cancer Genomics Studies with TCGA. International journal of molecular sciences, 20(5).
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  In the era of large genetic and genomic datasets, it has become crucially important to validate results of individual studies using data from publicly available sources, such as The Cancer Genome Atlas (TCGA). However, how generalizable are results from either an independent or a large public dataset to the remainder of the population? The study presented here aims to answer that question. Utilizing next generation sequencing data from endometrial and ovarian cancer patients from both the University of Iowa and TCGA, genomic admixture of each population was analyzed using STRUCTURE and ADMIXTURE software. In our independent data set, one subpopulation was identified, whereas in TCGA 4⁻6 subpopulations were identified. Data presented here demonstrate how different the genetic substructures of the TCGA and University of Iowa populations are. Validation of genomic studies between two different population samples must be aware of, account for and be corrected for background genetic substructure.
• Miller, M. D., Salinas, E. A., Newtson, A. M., Sharma, D., Keeney, M. E., Warrier, A., Smith, B. J., Bender, D. P., Goodheart, M. J., Thiel, K. W., Devor, E. J., Leslie, K. K., & Gonzalez-Bosquet, J. (2019). An integrated prediction model of recurrence in endometrial endometrioid cancers. Cancer management and research, 11, 5301-5315.
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  Endometrial cancer incidence and mortality are rising in the US. Disease recurrence has been shown to have a significant impact on mortality. However, to date, there are no accurate and validated prediction models that would discriminate which individual patients are likely to recur. Reliably predicting recurrence would be of benefit for treatment decisions following surgery. We present an integrated model constructed with comprehensive clinical, pathological and molecular features designed to discriminate risk of recurrence for patients with endometrioid endometrial adenocarcinoma. A cohort of endometrioid endometrial cancer patients treated at our institution was assembled. Clinical characteristics were extracted from patient charts. Primary tumors from these patients were obtained and total tissue RNA extracted for RNA sequencing. A prediction model was designed containing both clinical characteristics and molecular profiling of the tumors. The same analysis was carried out with data derived from The Cancer Genome Atlas for replication and external validation. Prediction models derived from our institutional data predicted recurrence with high accuracy as evidenced by areas under the curve approaching 1. Similar trends were observed in the analysis of TCGA data. Further, a scoring system for risk of recurrence was devised that showed specificities as high as 81% and negative predictive value as high as 90%. Lastly, we identify specific molecular characteristics of patient tumors that may contribute to the process of disease recurrence. By constructing a comprehensive model, we are able to reliably predict recurrence in endometrioid endometrial cancer. We devised a clinically useful scoring system and thresholds to discriminate risk of recurrence. Finally, the data presented here open a window to understanding the mechanisms of recurrence in endometrial cancer.
• Salinas, E. A., Miller, M. D., Newtson, A. M., Sharma, D., McDonald, M. E., Keeney, M. E., Smith, B. J., Bender, D. P., Goodheart, M. J., Thiel, K. W., Devor, E. J., Leslie, K. K., & Gonzalez Bosquet, J. (2019). A Prediction Model for Preoperative Risk Assessment in Endometrial Cancer Utilizing Clinical and Molecular Variables. International journal of molecular sciences, 20(5).
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  The utility of comprehensive surgical staging in patients with low risk disease has been questioned. Thus, a reliable means of determining risk would be quite useful. The aim of our study was to create the best performing prediction model to classify endometrioid endometrial cancer (EEC) patients into low or high risk using a combination of molecular and clinical-pathological variables. We then validated these models with publicly available datasets. Analyses between low and high risk EEC were performed using clinical and pathological data, gene and miRNA expression data, gene copy number variation and somatic mutation data. Variables were selected to be included in the prediction model of risk using cross-validation analysis; prediction models were then constructed using these variables. Model performance was assessed by area under the curve (AUC). Prediction models were validated using appropriate datasets in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A prediction model with only clinical variables performed at 88%. Integrating clinical and molecular data improved prediction performance up to 97%. The best prediction models included clinical, miRNA expression and/or somatic mutation data, and stratified pre-operative risk in EEC patients. Integrating molecular and clinical data improved the performance of prediction models to over 95%, resulting in potentially useful clinical tests.
• Devor, E. J., Cha, E., Warrier, A., Miller, M. D., Gonzalez-Bosquet, J., & Leslie, K. K. (2018). The miR-503 cluster is coordinately under-expressed in endometrial endometrioid adenocarcinoma and targets many oncogenes, cell cycle genes, DNA repair genes and chemotherapy response genes. OncoTargets and therapy, 11, 7205-7211.
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  The miR-503 miRNA cluster, located at Xq23.1, is composed of six miRNAs; miR-424, miR-503, miR-542, miR-450a-1, miR-450a-2 and miR-450b. Numerous studies have focused on the relationship of one or two members of the cluster and various human cancers. Here, we suggest that the entire cluster as a single coordinately expressed polycistron transcribed from a single promoter in endometrial endometrioid adenocarcinoma (EEA).
• Devor, E. J., Reyes, H. D., Gonzalez-Bosquet, J., Warrier, A., Kenzie, S. A., Ibik, N. V., Miller, M. D., Schickling, B. M., Goodheart, M. J., Thiel, K. W., & Leslie, K. K. (2017). Placenta-Specific Protein 1 Expression in Human Papillomavirus 16/18-Positive Cervical Cancers Is Associated With Tumor Histology. International journal of gynecological cancer : official journal of the International Gynecological Cancer Society, 27(4), 784-790.
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  Expression of the trophoblast-specific gene placenta-specific protein 1 (PLAC1) has been detected in a wide variety of cancers. However, to date, PLAC1 expression has not been shown in cervical cancer. We have carried out a preliminary study that shows for the first time that PLAC1 is expressed in cervical cancers.